Estimating Tree Heights Using Multi-baseline PolInSAR Data With Compensation for Temporal Decorrelation, Case Study: AfriSAR Campaign Data

This paper presents a multibaseline method to increase the accuracy of height estimation when using SAR tomographic data. It is based upon mitigating the temporal decorrelation induced by wind. The Fourier–Legendre function of different orders was fitted to each pixel as the structure function in the PCT model. It was combined with the motion standard deviation function from the random-motion-over ground (RMoG) model. L-band multibaseline data are used that were acquired during the AfriSAR campaign over La Lope National Park in Gabon with a height range between 0 and 60 m that has an average of 30 m and standard deviation of 15 m. The results were compared with those from the regular PCT model using the root mean square error (RMSE). Histograms were compared to the one obtained from Lidar height map. The average RMSE was equal to 7.5 m for the regular PCT model and to 5.6 m for the modified PCT model. We concluded that the accuracy of tree height estimation increased after modeling of temporal decorrelation. This is of value for future satellite missions that would collect tomographic data over forest areas.